CS 5263 Bioinformatics
Lecture 22
Introduction to Microarray
Outline
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What is microarray
Basic categories of microarray
How can microarray be used
Computational and statistical methods involved
in microarray
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Probe design
Image processing
Pre-processing
Differentially expressed gene identification
Clustering / classification
Network / pathway modeling
Gene expression
Reverse transcription (in lab)
Product is called cDNA
• Genes have different activities at different
time / location
• DNA Microarrays
– Measure gene transcription (amount of mRNA) in
a high-throughput fashion
– A surrogate of gene activity
Northern Blot
(an old technique for measuring mRNA expression)
1. mRNA extracted and
purified.
4. mRNA are
transferred from the
gel to a membrane.
2. mRNA loaded for
electrophoresis.
Lane 1: size standards.
Lane 2: RNA to be tested.
3. The gel is charged
and RNA “swim”
through gel according
to weight.
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+
5. A labeled probe
specific for the RNA
fragment is incubated
with the blot. So the
RNA of interest can be
detected.
Hybridization
Need relatively large amount of mRNA
http://www.escience.ws/b572/L13/north.html
RT-PCR (reverse transcription-polymerase chain reaction)
1. RNA is reverse transcribed to DNA.
2. PCR procedures can be used amplify DNA at exponential
rate.
3. Gel quantification for the amplified product.
---- an semi-quantitative method. Smaller amount of sample
needed.
See animation of RT-PCR:
http://www.bio.davidson.edu/courses/Immunology/Flash/RT_PCR.html
real-time RT-PCR
1. The PCR amplification can be monitored by fluorescence
in “real time”.
2. The fluorescence values recorded in each cycle represent
the amount of amplified product.
Often used to
validate
microarray
---- a quantitative method. The current most advanced and
accurate analysis for mRNA abundance. Usually used to
validate microarray result.
http://www.ambion.com/techlib/basics/rtpcr/
Limitation of the old techniques
1. Labor intensive
2. Can only detect up to dozens of genes.
(gene-by-gene analysis)
What is a Microarray
Gene 102
Conceptually similar to
(reverse) Northern blot
(Many) probes, rather than
mRNAs, are fixed on some
surface, in an ordered way
Gene 305
What is a microarray (2)
• A 2D array of DNA sequences
from thousands of genes
• Each spot has many copies of
same gene (probe)
• Allow mRNAs from a sample to
hybridize
• Measure number of
hybridizations per spot
Goals of a Microarray
Experiment
1. Find the genes that change expression
between experimental and control
samples
2. Classify samples based on a gene
expression profile
3. Find patterns: Groups of biologically
related genes that change expression
together across samples/treatments
Microarray categories
• cDNAs microarray
– Each probe is the cDNA of a gene (hundreds to
thousands bp)
– Stanford, Brown Lab
• Oligonucleotide microarray
– Each probe is a synthesized short DNA (uniquely
corresponding to a substring of a gene)
– Affymetrix: ~ 25mers
– Aglient: ~ 60 mers
• Others
Spotted cDNA microarray
Array Manufacturing
Each tube contains cDNAs corresponding to a unique
gene. Pre-amplified, and spotted onto a glass slide
Experiment
cy3
cy5
Data acquisition
Computer programs are used to process the image into digital signals.
• Segmentation: determine the boundary between signal and background
• Results: gene expression ratios between two samples
cDNA Microarray Methodology Animation
Affymetrix GeneChip®
Array Design
25-mer unique oligo
mismatch in the middle
nuclieotide
multiple probes (11~16) for each gene
from Affymetrix Inc.
Array Manufacturing
Technology adapted from semiconductor industry.
(photolithography and combinatorial chemistry)
In situ synthesis of oligonucletides
from Affymetrix Inc.
GeneChip Probe Arrays
®
Hybridized Probe Cell
GeneChip Probe Array
Single stranded,
labeled RNA target
* *
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Oligonucleotide probe
24µm
1.28cm
Millions of copies of a specific
oligonucleotide probe
>200,000 different
complementary probes
Image of Hybridized Probe Array
Overview of the Affymetrix GeneChip technology
Each probe set combines to give an
absolute expression level.
Image segmentation is relatively easy.
But how to use MM signal is debatable
from Affymetrix Inc.
Comparison of cDNA array and GeneChip
cDNA
GeneChip
Probe
preparation
Probes are cDNA fragments,
usually amplified by PCR and
spotted by robot.
Probes are short oligos
synthesized using a
photolithographic approach.
colors
Two-color
(measures relative intensity)
One-color
(measures absolute intensity)
Gene
representation
One probe per gene
11-16 probe pairs per gene
Probe length
Long, varying lengths
(hundreds to 1K bp)
25-mers
Density
Maximum of ~15000 probes.
38500 genes * 11 probes =
423500 probes
Affymetrix GeneChip
One color design
cDNA microarray
Two color design
Why the difference?
Affymetrix GeneChip
cDNA microarray
Photolithography
(The amount of oligos on a probe is well
controlled)
Robotic spotting
(The amount of cDNA spotted on a
probe may vary greatly)
Advantage and disadvantage of
cDNA array and GeneChip
cDNA microarray
Affymetrix GeneChip
The data can be noisy and with variable
quality
Specific and sensitive. Result very
reproducible.
Cross(non-specific) hybridization can
often happen.
Hybridization more specific.
May need a RNA amplification
procedure.
Can use small amount of RNA.
More difficulty in image analysis.
Image analysis and intensity extraction
is easier.
Need to search the database for gene
annotation.
More widely used. Better quality of gene
annotation.
Cheap. (both initial cost and per slide
cost)
Expensive (~$400 per array+labeling and
hybridization)
Can be custom made for special species. Only several popular species are
available
Do not need to know the exact DNA
sequence.
Need the DNA sequence for probe
selection.
Computational aspects
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Probe design
Image processing
Pre-processing
Differentially expressed gene identification
Clustering / classification
Network / pathway modeling
First step: pre-processing
• Transformation
– Transforms intensities or ratios to a different scale
– Why?
• For convenience
• Convert data into a certain distribution (e.g. normal) assumed
by many other statistical procedures
• Normalization
– Correct for systematic errors
– Make data from different samples comparable
Garbage in => Garbage out
Where errors could come from?
• Random errors
– Repeat the same experiment twice, get diff results
– Using multiple replicates reduces the problem
• Systematic errors
– Arrays manufactured at different time
– On the same array, probes printed with different
printer tips may have different biases
– Dye effect: difference between Cy5 and Cy3 labeling
– Experimental factors
• Array A being applied more mRNAs than array B
• Sample preparation procedure
• Experiments carried out at different time, by different users,
etc.
cDNA microarray data preprocessing
Typical experiments
Wide-type cells vs mutated cells
Diseased cells with normal cells
Cells under normal growth condition vs cells treated with chemicals
Typically repeated for several times
Ratios
Probes (genes)
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Transforming cDNA microarray data
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Data: Cy5/Cy3 ratios as well as raw intensities
Most common is log2 transformation
2 fold increase => log2(2) = 1
2 fold decrease => log2(1/2) = -1
1800
3500
1600
3000
1400
2500
Frequency
Frequency
1200
2000
1500
1000
800
600
1000
400
500
0
200
0
2
4
6
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Cy5/Cy3 ratio
10
12
14
0
-4
-3
-2
-1
0
1
log (Cy5/Cy3)
2
2
3
4
Dye effect
cDNA microarray experiments using two identical samples.
Cy5 consistently lower than Cy3. Solution: dye swapping.
Dye swapping
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Chip 1: label test by cy5 and control by cy3
Chip 2: label test by cy3 and control by cy5
Ideally cy5/cy3 = cy3/cy5
Not so due to dye effect
Compute average ratio:
½ log2 (cy5/cy3 on chip 1)
+ ½ log2 (cy3/cy5 on chip 2)
Total intensity normalization
• Even after dye-swapping, may still
see systematic biases
• Assume the total amount of
mRNAs should not change
between two samples
• House-keeping genes
• Middle 90% (for example) of genes
• Spike-in genes
2500
2500
2000
2000
Frequency
– Not necessarily true
– Rescale so that the two colors
have same total intensity
– Rescale according to a subset of
genes
3000
3000
1500
1500
1000
1000
500
500
00
-4
-4
-3
-3
-2
-2
-1
-1
00
11
log (Cy5/Cy3)
22
22
33
44
M-A plot
• Also know as ratio-intensity plot
• M: log2(cy5 / cy3) = log2(cy5) – log2(cy3)
• A: ½ log2(cy5 * cy3) = (log2(cy5) + log2(cy3)) / 2
Ideal:
• M centered at zero
• variance does not depend on A.
M
However:
• Systematic dependence between
M and A
A
• High variance of M for smaller A
Lowess normalization
• Lowess: Locally Weighted Regression
• Fit local polynomial functions
• M adjusted according to fitted line
M’
M
A
A
Replicate filtering
Ratio 1
Ratio 2
Log2(ratio2)
• Experiments repeated
• Genes with very high
variability is
questionable
Log2(ratio1)
oligo microarray data preprocessing
(Affymetrix chip)
Typical experiments
• Multiple microarrays
– n samples (from different time, location, condition,
treatment, etc.)
– k replicates for each samples
• For example
– Samples collected from 100 healthy people and 100
cancer patients
– Cells treated with some drugs, take samples every 10
minutes
• Repeat on 3 – 5 microarrays for each sample
– Improve reliability of the results
– Often averaged after some preprocessing
Main characteristics
• For each gene, there are multiple PM and
MM probes (11-16 pairs)
– how to obtain overall intensities from these
probe-level intensities?
• Array outputs are absolute values rather
than ratios
– Cross-array normalization is important for
them to be comparable
How to use MM information?
• Earlier approach:
– First remove outliner probes
– Actual intensity = Ipm – Imm
– IPM = IMM + Ispecific ?
• Recent trend
– Tend to ignore Imm
or use in a different way
900
800
• Various software packages
MAS5 (by affymetrix)
dChIP
RMA
GCRMA
600
MM
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700
500
400
300
200
100
0
0
500
1000
PM
1500
Normalization
• Similar to cDNA microarrays
• Total intensity normalization
– Each array has the same mean intensity
– Can be based on all genes or a selected subset of
genes
• House-keeping genes
• Middle 90% (for example) of genes
• Spike-in genes
• Lowess with a common reference
• Many useful tools implemented in Bioconductor
Conclusions
• Microarray provides a way to measure thousands
of genes simultaneously and make the global
monitoring of cellular activities possible.
• The method produces noisy data and
normalization is crucial.
• Real Time RT-PCR for validation of small number
of genes.
Limitation
• Measures mRNA instead of proteins. Actual
protein abundance and post-translation
modification can not be detected.
• Suitable for global monitoring and should be
used to generate further hypothesis or should
combine with other carefully designed
experiments.
Microarray preproc questions
• What kind of array it is?
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Two-color?
One-color?
Oligo array?
cDNA array?
• How is the experiment designed?
– Time series?
– Test vs control?
• What kind of preprocessing has been done?
– What value: raw intensity value or ratios?
– Transformation? Log scale? Linear scale?
– Normalization: within-array? Cross-array?
• What are the next steps you want to proceed?
– Identifying differentially expressed genes?
– Clustering?
Some real data
• Joseph L. DeRisi, Vishwanath R. Iyer,
Patrick O. Brown, “Exploring the
Metabolic and Genetic Control of Gene
Expression on a Genomic Scale”, Science,
278: 680 – 686, 1997